Elaborate computing systems are used to coordinate the display of online ads to visitors of web pages, search engine users, social media users, e-mail recipients, and other electronic device users. In a common example, a merchant (marketer) wanting to reach its customers (visitors) on some other company's website (publisher), like a sport television network's website, does so by bidding on one or more online ad-slots on the publisher's web pages. The publisher web pages have online ad-slots that are commonly auctioned via an online ad-exchange such as the DoubleClick Ad Exchange™ program by Google, Inc. of Mountain View, Calif. In addition, Demand Side Platforms (DSPs) such as Adobe® Media Optimizer, by Adobe Systems, Inc. of San Jose, Calif., place bids on behalf of marketers. When a visitor requests a publisher's web page, an online ad-exchange quickly runs an auction to a find a bidder. The online ad-exchange provides an online ad request that bidders bid upon and the winning bidder's (marketer's) online ad is displayed with the web page as an online ad impression. The visitor could then potentially take a desired action, such as clicking on the online ad, making a purchase on the marketer website, etc.
Every day there are billions of online ad requests requesting bids for available online ad-slots. Marketers and the automated systems that assist marketers continue to struggle to distinguish and select appropriate online ad requests for their ads. For example, showing an online ad about promotional offers in California to a person residing in India would be meaningless. On the other hand, it would make sense to display an online ad about tires on automobile related web pages.
Existing approaches attempt to select online ad requests based on context or user behavior. Contextual advertising approaches attempt to identify online ad requests by attempting to identify an online ad request context that matches the ad, for example, determining to place an ad for credit cards on webpages related to financial articles. Contextual advertising is generally based on the textual analysis of the webpage and thus requires extensive crawling of webpages followed by natural language processing. These processes require significant processing resources and time, making it infeasible to do contextual analysis of web pages, particularly in a real-time process that would need to respond to an online ad request very quickly. Behavioral advertising approaches attempt to identify online ad requests that involve users with particular characteristics. For example, a user's interests (e.g., sports and travel) are identified based on the webpages that he views. When an online ad request is received involving the user (e.g., the user accesses a webpage that has an open ad slot), a marketer with ads related to the user interests (e.g., sports and travel) places an ad there. These existing techniques for behavioral advertising, however, do not adequately address the sparsity of user data that is available. Data for a single user is sparse and generally insufficient to statistically deduce significant information about individual user interests, for example.
Existing techniques for selecting online ad requests to bid on also are unable to select online ad requests based on metric data about prior user actions for ads placed in response to online ad requests. Even though such performance metric data about user interactions is often tracked, existing approaches for selecting online requests are unable to identify characteristics of online ad requests to target using this data due to variance, sparsity, and volume issues in the data. With respect to data variance, for example, it may appear that visitors from California viewing ads on a particular sports website on a football related webpage yield high revenue on average. But the high average revenue may be largely due to a one-off purchase that should be considered an outlier.
Marketers are also unable to adequately address data sparsity. For example, it may appear that visitors from New York viewing ads on a news website on one of its news-related webpages do not yield much revenue. But the low revenue estimate may be due to the sample being only a few impressions.
Marketers are also unable to adequately address data volume differences. In one example, there are indicators of high revenue from a certain section of traffic, but the traffic might not be large enough to exhaust a campaign budget, while another section may have large enough traffic to exhaust an entire campaign budget within a few minutes. As another example, one might find that travel-related websites yield revenue of $10 for every 1000 impression, whereas news-related websites yield revenue of $1 for every 1000 impression. A marketer failing to take into account the volume differences may implement a strategy of bidding on both, which will consume the entire campaign budget on news-related websites within minutes rather than placing ads on the higher yielding travel-related website ad-slots that may trickle in slowly but with a sufficient pace to spend the budget over the day.